... behaviors, as indicated by the confusion matrix and a F-score of 0.869, showing great potential to enhance road safety. machine learning aggressive driving behavior classification driving simulator Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers...

... Abstract The inception of physics-constrained or physics-informed machine learning represents a paradigm shift, addressing the challenges associated with data scarcity and enhancing model interpretability. This innovative approach incorporates the fundamental laws of physics as constraints...

... in the skilled trade workforce. Specifically, the paper focuses on task recognition. Given the challenges of producing enough training data for machine learning (ML) using data purely from human-based testing, this paper shows how data synthetically-generated by a robot can be leveraged in the ML training...

... printing, aiming to enhance microstructure control and advance process automation. directed energy deposition melt pool temperature machine learning predictive modeling Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information...

... to illustrate the mitigation of misclassified information. The results demonstrate the effectiveness of utilizing environment features in combination with eye gaze to predict when the human needs AI assistance during a visually demanding task. human-AI teaming gaze machine learning Proceedings...

... Abstract Machine learning (ML)-based monitoring systems have been extensively developed to enhance the print quality of additive manufacturing (AM). In-situ and in-process data acquired using sensors can be used to train ML models that detect process anomalies, predict part quality, and adjust...

... to maintain balanced resources and structural relationships even under duress. Driven by the imperative to build sustainable infrastructure, this research explores the utilization of machine learning techniques to generate robust and reliable forecasts of green building specifications, even when design...

... the exhaustive search (enumeration-based) of all design cases. The results demonstrate a significant average reduction of over 92% in the number of system dynamic modeling and optimal control analyses required to identify optimal design scenarios. machine learning graph neural network graph regression...

... autoencoder machine learning Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2024 August 25-28, 2024, Washington, DC DETC2024-146347 GENERATIVE DESIGN OF PLANAR FOUR-BAR MOTIONS USING CONDITIONAL...

... accurate and robust predictions. material response unbalanced dataset microstructure encoding convolution neural networks fully connected neural networks machine learning Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information...

... an accurate digital model of the residual body and (2) reduce the effort of professionals in the 3D printing process, encouraging the implementation of 3D printing, especially in fused filament fabrication/Fused deposition modeling. prosthetics 3D printing machine learning Proceedings of the ASME...

... and show that it has potential to reduce the effort required to add to the PASE database by orders of magnitude, compared to the manual approach. product architecture data mining machine learning Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers...

... the potential of using LLMs to accelerate early-stage product development, reduce costs, and increase innovation. requirement elicitation LLMs simulation machine learning Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering...

..., is also proposed. human-robot collaboration disassembly score ease of disassembly machine learning automated rating systems remanufacturing Proceedings of the ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC...

... Abstract Electrostatically actuated MEMS tunable capacitors have a nonlinear capacitance-voltage (C-V) response and limited tunability. Increasing the tunability and the linearity of the device’s response is of great desire. In this study, a design method, based on machine learning...

... of traditional methods but also opens new avenues for mechanism design, providing a data-driven tool for exploring alternative designs and evaluating their performance in real-time. planar four-bar linkage path synthesis conditional variational autoencoder neural networks machine learning deep...

... Abstract In the recent past, various machine learning approaches have been broadly studied and successfully applied in the planar mechanism synthesis problem. This paves the way for its adoption in the spatial mechanism synthesis that is typically more complex to solve due to dimensionality...

... wavelets variational autoencoder neural networks machine learning deep learning Proceedings of the ASME 2023 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference IDETC-CIE2023 August 20-23, 2023, Boston, Massachusetts DETC2023-116892...

... properties and to re-measure the corresponding forces and pressures. This measurement-based safety assessment severely limits the flexibility of a collaborative process. In this paper, as a method for overcoming the flexibility problem, a physics guided machine learning ensemble for prediction of peak impact...

... Abstract Artificial neural networks (NNs) are a type of machine learning (ML) algorithm that mimics the functioning of the human brain to learn and generalize patterns from large amounts of data without the need for explicit knowledge of the system’s physics. Employing NNs to predict time...